AI costs surge as usage-based pricing catches companies off guard, budgets explode
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The hidden meter running on your AI
Most companies can tell you whether their artificial intelligence is online. Far fewer can tell you what it spent in the last hour, or why. They are content to find out at month's end, when the invoice lands. It's a mistake too many make. Treating AI cost as something you reconcile after the fact is how good products become unprofitable ones. The gap between a system that looks healthy and a system that is quietly expensive is the part of the AI era that has caught many leaders off guard. For decades, software costs were boring in the best way. You bought software, the price was roughly fixed, and you could plan a year ahead. AI broke that arrangement. Every interaction with a model consumes tokens, the small units of text it reads and writes, and you pay for each. With usage-based pricing, spend now moves with behavior rather than with a contract. The more your customers use the AI agent, and the more steps AI takes to answer them, the more it costs your organization. The meter is always running, and most organizations cannot see it move until it is too late to do anything about it. CHEAPER BY THE UNIT, BIGGER BY THE BILL The confusing part is that AI keeps getting cheaper to use while the bills keep climbing. Gartner forecasts that by 2030, running inference on a one-trillion-parameter model will cost providers more than 90% less than it did in 2025. Yet, enterprise AI spending is rising anyway, because consumption is growing faster than prices are dropping. When something becomes cheaper and more useful, people use far more of it.
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AI Was Supposed to Save Companies Money. Instead, It's Blowing Up Budgets in a Big Way
A survey from KPMG finds business owners are aghast at their bills for AI, now that many AI companies have shifted to a usage-based model. The accounting firm spoke with 2,145 executives around the world. And one-third said they had a limited understanding of usage costs. AI companies used to charge corporate clients a flat rate, but as compute costs have increased, many major operators are switching to a different model to help control costs. That wasn't factored into some executives' decisions to go all-in on the technology. "AI is now as much a financial management priority as it is a technology one," Rob Fisher, global head of advisory at KPMG, said in a statement. "The real risk isn't investing in AI but doing so without cost visibility and an understanding of the economics of AI. Organizations that have visibility into their costs and maintain strong oversight are the ones translating AI investment into real, measurable value."
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Companies are discovering that AI spending has become a financial management crisis rather than a cost-saving solution. A KPMG survey of 2,145 executives worldwide reveals one-third have limited understanding of their AI costs. The shift from flat-rate to usage-based pricing models has turned predictable software budgets into unpredictable expenses that grow with every model interaction and token consumed.
AI Costs Spiral as Companies Struggle With New Pricing Reality
The promise of AI as a cost-saving technology is colliding with an uncomfortable reality: companies are watching their budgets explode as usage-based pricing models replace traditional flat-rate agreements
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. What was once a predictable software expense has transformed into a constantly running meter that charges for every interaction, creating unexpected financial challenges for organizations that adopted AI without fully understanding its economic impact.A KPMG survey of 2,145 executives worldwide exposes the depth of this problem
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. One-third of business leaders admit they have limited understanding of their usage costs, discovering the true expense only when monthly invoices arrive. This lack of cost visibility has turned AI from a technology priority into a financial management crisis, with many organizations unable to track what their systems spent in the last hour or explain why.
Source: Inc.
The Shift From Predictable to Pay-Per-Use
For decades, software costs operated on a simple principle: companies bought licenses at fixed prices and planned budgets a year ahead
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. AI shattered that arrangement. Every model interaction now consumes tokens—small units of text the system reads and writes—and organizations pay for each one. The more customers use AI agents, and the more steps those agents take to answer queries, the higher the bill climbs. This token consumption creates a dynamic where spend moves with behavior rather than contracts, catching executives off guard who budgeted based on old assumptions.As compute costs have increased, major AI providers shifted away from flat rates to usage-based pricing models to help control their own expenses
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. This wasn't factored into many executives' decisions to go all-in on the technology, creating a gap between systems that appear healthy and those that are quietly blowing up budgets.
Source: Fast Company
The Paradox of Falling Prices and Rising Bills
A confusing paradox defines the current state of AI spending: unit costs are plummeting while total bills keep climbing
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. Gartner forecasts that by 2030, AI inference costs for running a one-trillion-parameter model will drop more than 90% compared to 2025 levels. Yet enterprise AI spending continues to rise because consumption is growing faster than prices are falling. When something becomes both cheaper and more useful, people use far more of it—a dynamic that turns cost savings into cost explosions.This creates operational risks for companies that treat AI costs as something to reconcile after the fact. The difference between a profitable product and an unprofitable one often lies in understanding these financial risks before they materialize on an invoice.
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What Organizations Must Watch
Rob Fisher, global head of advisory at KPMG, frames the challenge clearly: "AI is now as much a financial management priority as it is a technology one. The real risk isn't investing in AI but doing so without cost visibility and an understanding of the economics of AI"
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. Organizations that maintain strong oversight and understand their spending patterns are the ones translating AI investment into measurable value rather than budget overruns.The short-term implication is clear: companies need immediate visibility into their AI costs or risk discovering profitability problems too late to address them. Long-term, managing AI costs will require new financial controls and monitoring systems that track consumption in real-time rather than monthly. As AI becomes more deeply embedded in business operations, the organizations that master cost visibility will separate themselves from those still waiting for invoices to understand what they spent and why.
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